"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from copy import deepcopy
from functools import partial
from typing import Tuple, List

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_
from .registry import register_model


__all__ = [
    "coat_tiny",
    "coat_mini",
    "coat_lite_tiny",
    "coat_lite_mini",
    "coat_lite_small"
]


def _cfg_coat(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed1.proj', 'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    'coat_tiny': _cfg_coat(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_tiny-473c2a20.pth'
    ),
    'coat_mini': _cfg_coat(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_mini-2c6baf49.pth'
    ),
    'coat_lite_tiny': _cfg_coat(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_tiny-461b07a7.pth'
    ),
    'coat_lite_mini': _cfg_coat(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_mini-d7842000.pth'
    ),
    'coat_lite_small': _cfg_coat(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_small-fea1d5a1.pth'
    ),
}


class ConvRelPosEnc(nn.Module):
    """ Convolutional relative position encoding. """
    def __init__(self, Ch, h, window):
        """
        Initialization.
            Ch: Channels per head.
            h: Number of heads.
            window: Window size(s) in convolutional relative positional encoding. It can have two forms:
                1. An integer of window size, which assigns all attention heads with the same window s
                    size in ConvRelPosEnc.
                2. A dict mapping window size to #attention head splits (
                    e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})
                    It will apply different window size to the attention head splits.
        """
        super().__init__()

        if isinstance(window, int):
            # Set the same window size for all attention heads.
            window = {window: h}
            self.window = window
        elif isinstance(window, dict):
            self.window = window
        else:
            raise ValueError()            
        
        self.conv_list = nn.ModuleList()
        self.head_splits = []
        for cur_window, cur_head_split in window.items():
            dilation = 1
            # Determine padding size.
            # Ref: https://discuss.pytorch.org/t/how-to-keep-the-shape-of-input-and-output-same-when-dilation-conv/14338
            padding_size = (cur_window + (cur_window - 1) * (dilation - 1)) // 2
            cur_conv = nn.Conv2d(cur_head_split*Ch, cur_head_split*Ch,
                kernel_size=(cur_window, cur_window), 
                padding=(padding_size, padding_size),
                dilation=(dilation, dilation),                          
                groups=cur_head_split*Ch,
            )
            self.conv_list.append(cur_conv)
            self.head_splits.append(cur_head_split)
        self.channel_splits = [x*Ch for x in self.head_splits]

    def forward(self, q, v, size: Tuple[int, int]):
        B, h, N, Ch = q.shape
        H, W = size
        assert N == 1 + H * W

        # Convolutional relative position encoding.
        q_img = q[:, :, 1:, :]  # [B, h, H*W, Ch]
        v_img = v[:, :, 1:, :]  # [B, h, H*W, Ch]

        v_img = v_img.transpose(-1, -2).reshape(B, h * Ch, H, W)
        v_img_list = torch.split(v_img, self.channel_splits, dim=1)  # Split according to channels
        conv_v_img_list = []
        for i, conv in enumerate(self.conv_list):
            conv_v_img_list.append(conv(v_img_list[i]))
        conv_v_img = torch.cat(conv_v_img_list, dim=1)
        conv_v_img = conv_v_img.reshape(B, h, Ch, H * W).transpose(-1, -2)

        EV_hat = q_img * conv_v_img
        EV_hat = F.pad(EV_hat, (0, 0, 1, 0, 0, 0))  # [B, h, N, Ch].
        return EV_hat


class FactorAtt_ConvRelPosEnc(nn.Module):
    """ Factorized attention with convolutional relative position encoding class. """
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., shared_crpe=None):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)  # Note: attn_drop is actually not used.
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        # Shared convolutional relative position encoding.
        self.crpe = shared_crpe

    def forward(self, x, size: Tuple[int, int]):
        B, N, C = x.shape

        # Generate Q, K, V.
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # [B, h, N, Ch]

        # Factorized attention.
        k_softmax = k.softmax(dim=2)
        factor_att = k_softmax.transpose(-1, -2) @ v
        factor_att = q @ factor_att

        # Convolutional relative position encoding.
        crpe = self.crpe(q, v, size=size)  # [B, h, N, Ch]

        # Merge and reshape.
        x = self.scale * factor_att + crpe
        x = x.transpose(1, 2).reshape(B, N, C)  # [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C]

        # Output projection.
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class ConvPosEnc(nn.Module):
    """ Convolutional Position Encoding. 
        Note: This module is similar to the conditional position encoding in CPVT.
    """
    def __init__(self, dim, k=3):
        super(ConvPosEnc, self).__init__()
        self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim) 
    
    def forward(self, x, size: Tuple[int, int]):
        B, N, C = x.shape
        H, W = size
        assert N == 1 + H * W

        # Extract CLS token and image tokens.
        cls_token, img_tokens = x[:, :1], x[:, 1:]  # [B, 1, C], [B, H*W, C]
        
        # Depthwise convolution.
        feat = img_tokens.transpose(1, 2).view(B, C, H, W)
        x = self.proj(feat) + feat
        x = x.flatten(2).transpose(1, 2)

        # Combine with CLS token.
        x = torch.cat((cls_token, x), dim=1)

        return x


class SerialBlock(nn.Module):
    """ Serial block class.
        Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module. """
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, shared_cpe=None, shared_crpe=None):
        super().__init__()

        # Conv-Attention.
        self.cpe = shared_cpe

        self.norm1 = norm_layer(dim)
        self.factoratt_crpe = FactorAtt_ConvRelPosEnc(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, shared_crpe=shared_crpe)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        # MLP.
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x, size: Tuple[int, int]):
        # Conv-Attention.
        x = self.cpe(x, size)
        cur = self.norm1(x)
        cur = self.factoratt_crpe(cur, size)
        x = x + self.drop_path(cur) 

        # MLP. 
        cur = self.norm2(x)
        cur = self.mlp(cur)
        x = x + self.drop_path(cur)

        return x


class ParallelBlock(nn.Module):
    """ Parallel block class. """
    def __init__(self, dims, num_heads, mlp_ratios=[], qkv_bias=False, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, shared_crpes=None):
        super().__init__()

        # Conv-Attention.
        self.norm12 = norm_layer(dims[1])
        self.norm13 = norm_layer(dims[2])
        self.norm14 = norm_layer(dims[3])
        self.factoratt_crpe2 = FactorAtt_ConvRelPosEnc(
            dims[1], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
            shared_crpe=shared_crpes[1]
        )
        self.factoratt_crpe3 = FactorAtt_ConvRelPosEnc(
            dims[2], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
            shared_crpe=shared_crpes[2]
        )
        self.factoratt_crpe4 = FactorAtt_ConvRelPosEnc(
            dims[3], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
            shared_crpe=shared_crpes[3]
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        # MLP.
        self.norm22 = norm_layer(dims[1])
        self.norm23 = norm_layer(dims[2])
        self.norm24 = norm_layer(dims[3])
        # In parallel block, we assume dimensions are the same and share the linear transformation.
        assert dims[1] == dims[2] == dims[3]
        assert mlp_ratios[1] == mlp_ratios[2] == mlp_ratios[3]
        mlp_hidden_dim = int(dims[1] * mlp_ratios[1])
        self.mlp2 = self.mlp3 = self.mlp4 = Mlp(
            in_features=dims[1], hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def upsample(self, x, factor: float, size: Tuple[int, int]):
        """ Feature map up-sampling. """
        return self.interpolate(x, scale_factor=factor, size=size)

    def downsample(self, x, factor: float, size: Tuple[int, int]):
        """ Feature map down-sampling. """
        return self.interpolate(x, scale_factor=1.0/factor, size=size)

    def interpolate(self, x, scale_factor: float, size: Tuple[int, int]):
        """ Feature map interpolation. """
        B, N, C = x.shape
        H, W = size
        assert N == 1 + H * W

        cls_token = x[:, :1, :]
        img_tokens = x[:, 1:, :]
        
        img_tokens = img_tokens.transpose(1, 2).reshape(B, C, H, W)
        img_tokens = F.interpolate(
            img_tokens, scale_factor=scale_factor, recompute_scale_factor=False, mode='bilinear', align_corners=False)
        img_tokens = img_tokens.reshape(B, C, -1).transpose(1, 2)
        
        out = torch.cat((cls_token, img_tokens), dim=1)

        return out

    def forward(self, x1, x2, x3, x4, sizes: List[Tuple[int, int]]):
        _, S2, S3, S4 = sizes
        cur2 = self.norm12(x2)
        cur3 = self.norm13(x3)
        cur4 = self.norm14(x4)
        cur2 = self.factoratt_crpe2(cur2, size=S2)
        cur3 = self.factoratt_crpe3(cur3, size=S3)
        cur4 = self.factoratt_crpe4(cur4, size=S4)
        upsample3_2 = self.upsample(cur3, factor=2., size=S3)
        upsample4_3 = self.upsample(cur4, factor=2., size=S4)
        upsample4_2 = self.upsample(cur4, factor=4., size=S4)
        downsample2_3 = self.downsample(cur2, factor=2., size=S2)
        downsample3_4 = self.downsample(cur3, factor=2., size=S3)
        downsample2_4 = self.downsample(cur2, factor=4., size=S2)
        cur2 = cur2 + upsample3_2 + upsample4_2
        cur3 = cur3 + upsample4_3 + downsample2_3
        cur4 = cur4 + downsample3_4 + downsample2_4
        x2 = x2 + self.drop_path(cur2) 
        x3 = x3 + self.drop_path(cur3) 
        x4 = x4 + self.drop_path(cur4) 

        # MLP. 
        cur2 = self.norm22(x2)
        cur3 = self.norm23(x3)
        cur4 = self.norm24(x4)
        cur2 = self.mlp2(cur2)
        cur3 = self.mlp3(cur3)
        cur4 = self.mlp4(cur4)
        x2 = x2 + self.drop_path(cur2)
        x3 = x3 + self.drop_path(cur3)
        x4 = x4 + self.drop_path(cur4) 

        return x1, x2, x3, x4


class CoaT(nn.Module):
    """ CoaT class. """
    def __init__(
            self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=(0, 0, 0, 0), 
            serial_depths=(0, 0, 0, 0), parallel_depth=0, num_heads=0, mlp_ratios=(0, 0, 0, 0), qkv_bias=True,
            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
            return_interm_layers=False, out_features=None, crpe_window=None, **kwargs):
        super().__init__()
        crpe_window = crpe_window or {3: 2, 5: 3, 7: 3}
        self.return_interm_layers = return_interm_layers
        self.out_features = out_features
        self.embed_dims = embed_dims
        self.num_features = embed_dims[-1]
        self.num_classes = num_classes

        # Patch embeddings.
        img_size = to_2tuple(img_size)
        self.patch_embed1 = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans,
            embed_dim=embed_dims[0], norm_layer=nn.LayerNorm)
        self.patch_embed2 = PatchEmbed(
            img_size=[x // 4 for x in img_size], patch_size=2, in_chans=embed_dims[0],
            embed_dim=embed_dims[1], norm_layer=nn.LayerNorm)
        self.patch_embed3 = PatchEmbed(
            img_size=[x // 8 for x in img_size], patch_size=2, in_chans=embed_dims[1],
            embed_dim=embed_dims[2], norm_layer=nn.LayerNorm)
        self.patch_embed4 = PatchEmbed(
            img_size=[x // 16 for x in img_size], patch_size=2, in_chans=embed_dims[2],
            embed_dim=embed_dims[3], norm_layer=nn.LayerNorm)

        # Class tokens.
        self.cls_token1 = nn.Parameter(torch.zeros(1, 1, embed_dims[0]))
        self.cls_token2 = nn.Parameter(torch.zeros(1, 1, embed_dims[1]))
        self.cls_token3 = nn.Parameter(torch.zeros(1, 1, embed_dims[2]))
        self.cls_token4 = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))

        # Convolutional position encodings.
        self.cpe1 = ConvPosEnc(dim=embed_dims[0], k=3)
        self.cpe2 = ConvPosEnc(dim=embed_dims[1], k=3)
        self.cpe3 = ConvPosEnc(dim=embed_dims[2], k=3)
        self.cpe4 = ConvPosEnc(dim=embed_dims[3], k=3)

        # Convolutional relative position encodings.
        self.crpe1 = ConvRelPosEnc(Ch=embed_dims[0] // num_heads, h=num_heads, window=crpe_window)
        self.crpe2 = ConvRelPosEnc(Ch=embed_dims[1] // num_heads, h=num_heads, window=crpe_window)
        self.crpe3 = ConvRelPosEnc(Ch=embed_dims[2] // num_heads, h=num_heads, window=crpe_window)
        self.crpe4 = ConvRelPosEnc(Ch=embed_dims[3] // num_heads, h=num_heads, window=crpe_window)

        # Disable stochastic depth.
        dpr = drop_path_rate
        assert dpr == 0.0
        
        # Serial blocks 1.
        self.serial_blocks1 = nn.ModuleList([
            SerialBlock(
                dim=embed_dims[0], num_heads=num_heads, mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer, 
                shared_cpe=self.cpe1, shared_crpe=self.crpe1
            )
            for _ in range(serial_depths[0])]
        )

        # Serial blocks 2.
        self.serial_blocks2 = nn.ModuleList([
            SerialBlock(
                dim=embed_dims[1], num_heads=num_heads, mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer, 
                shared_cpe=self.cpe2, shared_crpe=self.crpe2
            )
            for _ in range(serial_depths[1])]
        )

        # Serial blocks 3.
        self.serial_blocks3 = nn.ModuleList([
            SerialBlock(
                dim=embed_dims[2], num_heads=num_heads, mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer, 
                shared_cpe=self.cpe3, shared_crpe=self.crpe3
            )
            for _ in range(serial_depths[2])]
        )

        # Serial blocks 4.
        self.serial_blocks4 = nn.ModuleList([
            SerialBlock(
                dim=embed_dims[3], num_heads=num_heads, mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer, 
                shared_cpe=self.cpe4, shared_crpe=self.crpe4
            )
            for _ in range(serial_depths[3])]
        )

        # Parallel blocks.
        self.parallel_depth = parallel_depth
        if self.parallel_depth > 0:
            self.parallel_blocks = nn.ModuleList([
                ParallelBlock(
                    dims=embed_dims, num_heads=num_heads, mlp_ratios=mlp_ratios, qkv_bias=qkv_bias,
                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
                    shared_crpes=(self.crpe1, self.crpe2, self.crpe3, self.crpe4)
                )
                for _ in range(parallel_depth)]
            )
        else:
            self.parallel_blocks = None

        # Classification head(s).
        if not self.return_interm_layers:
            if self.parallel_blocks is not None:
                self.norm2 = norm_layer(embed_dims[1])
                self.norm3 = norm_layer(embed_dims[2])
            else:
                self.norm2 = self.norm3 = None
            self.norm4 = norm_layer(embed_dims[3])

            if self.parallel_depth > 0:
                # CoaT series: Aggregate features of last three scales for classification.
                assert embed_dims[1] == embed_dims[2] == embed_dims[3]
                self.aggregate = torch.nn.Conv1d(in_channels=3, out_channels=1, kernel_size=1)
                self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
            else:
                # CoaT-Lite series: Use feature of last scale for classification.
                self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

        # Initialize weights.
        trunc_normal_(self.cls_token1, std=.02)
        trunc_normal_(self.cls_token2, std=.02)
        trunc_normal_(self.cls_token3, std=.02)
        trunc_normal_(self.cls_token4, std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'cls_token1', 'cls_token2', 'cls_token3', 'cls_token4'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def insert_cls(self, x, cls_token):
        """ Insert CLS token. """
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        return x

    def remove_cls(self, x):
        """ Remove CLS token. """
        return x[:, 1:, :]

    def forward_features(self, x0):
        B = x0.shape[0]

        # Serial blocks 1.
        x1 = self.patch_embed1(x0)
        H1, W1 = self.patch_embed1.grid_size
        x1 = self.insert_cls(x1, self.cls_token1)
        for blk in self.serial_blocks1:
            x1 = blk(x1, size=(H1, W1))
        x1_nocls = self.remove_cls(x1)
        x1_nocls = x1_nocls.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
        
        # Serial blocks 2.
        x2 = self.patch_embed2(x1_nocls)
        H2, W2 = self.patch_embed2.grid_size
        x2 = self.insert_cls(x2, self.cls_token2)
        for blk in self.serial_blocks2:
            x2 = blk(x2, size=(H2, W2))
        x2_nocls = self.remove_cls(x2)
        x2_nocls = x2_nocls.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()

        # Serial blocks 3.
        x3 = self.patch_embed3(x2_nocls)
        H3, W3 = self.patch_embed3.grid_size
        x3 = self.insert_cls(x3, self.cls_token3)
        for blk in self.serial_blocks3:
            x3 = blk(x3, size=(H3, W3))
        x3_nocls = self.remove_cls(x3)
        x3_nocls = x3_nocls.reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()

        # Serial blocks 4.
        x4 = self.patch_embed4(x3_nocls)
        H4, W4 = self.patch_embed4.grid_size
        x4 = self.insert_cls(x4, self.cls_token4)
        for blk in self.serial_blocks4:
            x4 = blk(x4, size=(H4, W4))
        x4_nocls = self.remove_cls(x4)
        x4_nocls = x4_nocls.reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()

        # Only serial blocks: Early return.
        if self.parallel_blocks is None:
            if not torch.jit.is_scripting() and self.return_interm_layers:
                # Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
                feat_out = {}   
                if 'x1_nocls' in self.out_features:
                    feat_out['x1_nocls'] = x1_nocls
                if 'x2_nocls' in self.out_features:
                    feat_out['x2_nocls'] = x2_nocls
                if 'x3_nocls' in self.out_features:
                    feat_out['x3_nocls'] = x3_nocls
                if 'x4_nocls' in self.out_features:
                    feat_out['x4_nocls'] = x4_nocls
                return feat_out
            else:
                # Return features for classification.
                x4 = self.norm4(x4)
                x4_cls = x4[:, 0]
                return x4_cls

        # Parallel blocks.
        for blk in self.parallel_blocks:
            x2, x3, x4 = self.cpe2(x2, (H2, W2)), self.cpe3(x3, (H3, W3)), self.cpe4(x4, (H4, W4))
            x1, x2, x3, x4 = blk(x1, x2, x3, x4, sizes=[(H1, W1), (H2, W2), (H3, W3), (H4, W4)])

        if not torch.jit.is_scripting() and self.return_interm_layers:
            # Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
            feat_out = {}   
            if 'x1_nocls' in self.out_features:
                x1_nocls = self.remove_cls(x1)
                x1_nocls = x1_nocls.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
                feat_out['x1_nocls'] = x1_nocls
            if 'x2_nocls' in self.out_features:
                x2_nocls = self.remove_cls(x2)
                x2_nocls = x2_nocls.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
                feat_out['x2_nocls'] = x2_nocls
            if 'x3_nocls' in self.out_features:
                x3_nocls = self.remove_cls(x3)
                x3_nocls = x3_nocls.reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
                feat_out['x3_nocls'] = x3_nocls
            if 'x4_nocls' in self.out_features:
                x4_nocls = self.remove_cls(x4)
                x4_nocls = x4_nocls.reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
                feat_out['x4_nocls'] = x4_nocls
            return feat_out
        else:
            x2 = self.norm2(x2)
            x3 = self.norm3(x3)
            x4 = self.norm4(x4)
            x2_cls = x2[:, :1]  # [B, 1, C]
            x3_cls = x3[:, :1]
            x4_cls = x4[:, :1]
            merged_cls = torch.cat((x2_cls, x3_cls, x4_cls), dim=1)  # [B, 3, C]
            merged_cls = self.aggregate(merged_cls).squeeze(dim=1)  # Shape: [B, C]
            return merged_cls

    def forward(self, x):
        if self.return_interm_layers:
            # Return intermediate features (for down-stream tasks).
            return self.forward_features(x)
        else:
            # Return features for classification.
            x = self.forward_features(x) 
            x = self.head(x)
            return x


def checkpoint_filter_fn(state_dict, model):
    out_dict = {}
    for k, v in state_dict.items():
        # original model had unused norm layers, removing them requires filtering pretrained checkpoints
        if k.startswith('norm1') or \
                (model.norm2 is None and k.startswith('norm2')) or \
                (model.norm3 is None and k.startswith('norm3')):
            continue
        out_dict[k] = v
    return out_dict


def _create_coat(variant, pretrained=False, default_cfg=None, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')

    model = build_model_with_cfg(
        CoaT, variant, pretrained,
        default_cfg=default_cfgs[variant],
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs)
    return model


@register_model
def coat_tiny(pretrained=False, **kwargs):
    model_cfg = dict(
        patch_size=4, embed_dims=[152, 152, 152, 152], serial_depths=[2, 2, 2, 2], parallel_depth=6,
        num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
    model = _create_coat('coat_tiny', pretrained=pretrained, **model_cfg)
    return model


@register_model
def coat_mini(pretrained=False, **kwargs):
    model_cfg = dict(
        patch_size=4, embed_dims=[152, 216, 216, 216], serial_depths=[2, 2, 2, 2], parallel_depth=6,
        num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
    model = _create_coat('coat_mini', pretrained=pretrained, **model_cfg)
    return model


@register_model
def coat_lite_tiny(pretrained=False, **kwargs):
    model_cfg = dict(
        patch_size=4, embed_dims=[64, 128, 256, 320], serial_depths=[2, 2, 2, 2], parallel_depth=0,
        num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
    model = _create_coat('coat_lite_tiny', pretrained=pretrained, **model_cfg)
    return model


@register_model
def coat_lite_mini(pretrained=False, **kwargs):
    model_cfg = dict(
        patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[2, 2, 2, 2], parallel_depth=0,
        num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
    model = _create_coat('coat_lite_mini', pretrained=pretrained, **model_cfg)
    return model


@register_model
def coat_lite_small(pretrained=False, **kwargs):
    model_cfg = dict(
        patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0,
        num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
    model = _create_coat('coat_lite_small', pretrained=pretrained, **model_cfg)
    return model